Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "146" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 96 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 94 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2459913 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 12.996209 | -0.903114 | 3.459370 | -0.174586 | 8.992910 | -0.406744 | 5.211365 | -0.946866 | 0.0375 | 0.6947 | 0.5601 | nan | nan |
| 2459912 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 10.998839 | 1.394442 | 3.027590 | 0.541737 | 8.690206 | -1.903316 | 0.069357 | -1.601613 | 0.0397 | 0.6953 | 0.5594 | nan | nan |
| 2459911 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 10.503946 | 1.434096 | 3.405839 | 0.803356 | 8.506085 | -1.651259 | 0.133066 | -1.258512 | 0.0381 | 0.6939 | 0.5666 | nan | nan |
| 2459910 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 10.777480 | 1.496790 | 3.431589 | 0.746911 | 8.664141 | -1.791458 | -0.313343 | -1.664522 | 0.0379 | 0.6959 | 0.5677 | nan | nan |
| 2459909 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 11.328301 | 1.551096 | 3.320477 | 0.483450 | 7.485413 | -1.796558 | 0.353600 | -1.396030 | 0.0375 | 0.6953 | 0.5698 | nan | nan |
| 2459908 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 11.939081 | 1.563559 | 3.249539 | 0.480914 | 6.330426 | -1.556103 | 0.230730 | -1.686125 | 0.0383 | 0.7030 | 0.5722 | nan | nan |
| 2459907 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 11.919889 | 1.661008 | 3.462864 | 0.540458 | 6.835219 | -1.382163 | 0.202401 | -1.596136 | 0.0414 | 0.7004 | 0.5633 | nan | nan |
| 2459906 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 11.719955 | 1.674757 | 3.546029 | 1.146386 | 7.897213 | -0.520301 | 0.165736 | -1.873263 | 0.0380 | 0.6934 | 0.5556 | nan | nan |
| 2459905 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 11.222831 | 2.062647 | 3.487207 | 1.044269 | 12.234494 | -1.216437 | -0.007419 | -1.286590 | 0.0363 | 0.6871 | 0.4914 | nan | nan |
| 2459904 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 10.976570 | 2.010965 | 3.835416 | 1.329753 | 8.933279 | -0.601528 | 0.713915 | -2.563745 | 0.0367 | 0.6869 | 0.4931 | nan | nan |
| 2459903 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 11.700894 | 2.170030 | 3.892998 | 1.011987 | 7.174490 | -0.505260 | 0.887067 | -2.686552 | 0.0371 | 0.6894 | 0.5120 | nan | nan |
| 2459902 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 13.301611 | 2.702526 | 4.079172 | 1.429914 | 7.761811 | -0.676198 | 0.361811 | -1.672315 | 0.0401 | 0.6904 | 0.5098 | nan | nan |
| 2459901 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.699534 | 1.664788 | -1.522950 | 0.662176 | 0.845884 | -1.169747 | 1.446195 | -1.899978 | 0.6328 | 0.6838 | 0.4193 | nan | nan |
| 2459900 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.113182 | 1.459163 | -1.445085 | 0.688898 | 0.438335 | -0.920086 | 0.321857 | -1.798308 | 0.5913 | 0.6533 | 0.3496 | nan | nan |
| 2459898 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.227786 | 1.152016 | -1.633162 | 0.747333 | -0.071346 | -1.324248 | -0.002536 | -2.247662 | 0.6456 | 0.6939 | 0.4066 | nan | nan |
| 2459897 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | - | - | 0.275121 | 1.188359 | -1.668448 | 0.914850 | -0.624371 | -0.955651 | 0.334594 | -2.282454 | 0.1010 | 0.1197 | 0.0548 | nan | nan |
| 2459896 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | - | - | -0.004576 | 1.329176 | -1.671049 | 0.937330 | -0.794360 | -1.531837 | -0.043724 | -1.639437 | 0.0999 | 0.1140 | 0.0525 | nan | nan |
| 2459895 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | - | - | 0.318098 | 0.958281 | -1.726955 | 1.833703 | -0.421830 | -0.171725 | -1.884193 | 0.484220 | 0.1114 | 0.1282 | 0.0346 | nan | nan |
| 2459894 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | - | - | 0.333531 | 1.217780 | -1.529503 | 0.856678 | -0.448507 | -1.194829 | 0.256120 | -2.199507 | 0.1017 | 0.1151 | 0.0498 | nan | nan |
| 2459893 | digital_ok | 0.00% | 38.49% | 38.49% | 0.00% | - | - | -0.112452 | 1.300633 | -1.606534 | 0.678657 | -0.533525 | -1.213404 | 0.008464 | -2.530768 | 0.4653 | 0.4899 | 0.2580 | nan | nan |
| 2459892 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.011255 | 1.309914 | -1.892218 | 0.966248 | 0.669324 | -1.003823 | -0.105448 | -2.405611 | 0.6571 | 0.6995 | 0.3907 | nan | nan |
| 2459891 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.067478 | 1.021531 | -1.860822 | 1.170873 | 0.442497 | -1.141120 | -0.004066 | -1.913772 | 0.6501 | 0.6956 | 0.3962 | nan | nan |
| 2459890 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.248244 | 1.046265 | -1.926435 | 1.730112 | 0.077643 | -1.064364 | 0.708997 | -1.087683 | 0.6489 | 0.6937 | 0.3963 | nan | nan |
| 2459889 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.208538 | 1.151233 | -1.911166 | 1.104184 | -0.509165 | -1.281256 | -0.057743 | -2.706080 | 0.6589 | 0.6986 | 0.3919 | nan | nan |
| 2459888 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.045160 | 0.906360 | -1.857964 | 1.536025 | -0.596093 | -1.033774 | -0.760842 | -0.784324 | 0.6784 | 0.7135 | 0.3839 | nan | nan |
| 2459887 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.006585 | 0.763230 | -1.748722 | 0.780660 | -1.218903 | -1.650748 | -0.094876 | -1.533122 | 0.6618 | 0.6985 | 0.3934 | nan | nan |
| 2459886 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.638546 | 0.945657 | -1.518109 | 1.145866 | -2.024161 | 0.308826 | -1.451006 | -0.282474 | 0.7540 | 0.7581 | 0.3241 | nan | nan |
| 2459885 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 2.232008 | 3.150252 | 7.137951 | 30.100266 | 1.774593 | 5.432572 | 2.236833 | 5.182172 | 0.7189 | 0.7368 | 0.3441 | nan | nan |
| 2459884 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -0.681848 | 4.619191 | -0.105304 | 4.661808 | -0.446535 | 5.239465 | -0.763065 | -3.406010 | 0.6787 | 0.6805 | 0.3861 | nan | nan |
| 2459883 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 1.057583 | 7.462584 | 17.312333 | 41.419800 | 1.240675 | 7.541308 | -0.650059 | -6.293574 | 0.6855 | 0.6897 | 0.3736 | nan | nan |
| 2459882 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 1.535877 | 12.043612 | 19.204940 | 48.558674 | 2.383712 | 10.541547 | 1.593897 | -2.773263 | 0.6884 | 0.6958 | 0.3692 | nan | nan |
| 2459881 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.582591 | 7.754697 | 21.797512 | 54.818014 | 2.956958 | 20.338903 | 0.378033 | 1.621760 | 0.7243 | 0.7402 | 0.3200 | nan | nan |
| 2459880 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.721808 | 8.437421 | 17.791784 | 44.378717 | 2.158732 | 6.506531 | -0.321799 | -3.877408 | 0.6826 | 0.6927 | 0.3842 | nan | nan |
| 2459879 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -1.288895 | 3.426176 | -2.337281 | 1.868955 | -0.296760 | -0.330627 | -0.733047 | -4.539379 | 0.6675 | 0.6801 | 0.4009 | nan | nan |
| 2459878 | digital_ok | 100.00% | 0.00% | 0.00% | 100.00% | - | - | 4.384917 | 7.399156 | 41.581275 | 51.565712 | 4.611827 | 12.850746 | -3.632006 | -6.882099 | 0.2694 | 0.2652 | -0.2791 | nan | nan |
| 2459876 | digital_ok | 100.00% | 0.00% | 0.00% | 100.00% | - | - | 4.649231 | 7.145446 | 29.695605 | 35.724585 | 10.443904 | 35.229634 | -3.177434 | -5.970377 | 0.2930 | 0.2835 | -0.2804 | nan | nan |
| 2459875 | digital_ok | 100.00% | 0.00% | 0.00% | 100.00% | - | - | 4.833955 | 7.219080 | 40.104959 | 48.771853 | 4.108412 | 17.715354 | -0.725041 | 4.061925 | 0.3078 | 0.3010 | -0.2855 | nan | nan |
| 2459874 | digital_ok | 100.00% | 0.00% | 0.00% | 100.00% | - | - | 6.874515 | 10.232680 | 20.682236 | 24.971760 | 5.383243 | 22.348143 | -2.591554 | -4.859304 | 0.2947 | 0.2904 | -0.2895 | nan | nan |
| 2459873 | digital_ok | 100.00% | 0.00% | 0.00% | 100.00% | - | - | 4.988978 | 7.218835 | 27.824572 | 33.605619 | 2.237567 | 9.191835 | -1.990780 | -3.041155 | 0.3193 | 0.3154 | -0.2917 | nan | nan |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 146 | N14 | digital_ok | ee Shape | 12.996209 | -0.903114 | 12.996209 | -0.174586 | 3.459370 | -0.406744 | 8.992910 | -0.946866 | 5.211365 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 146 | N14 | digital_ok | ee Shape | 10.998839 | 1.394442 | 10.998839 | 0.541737 | 3.027590 | -1.903316 | 8.690206 | -1.601613 | 0.069357 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 146 | N14 | digital_ok | ee Shape | 10.503946 | 1.434096 | 10.503946 | 0.803356 | 3.405839 | -1.651259 | 8.506085 | -1.258512 | 0.133066 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 146 | N14 | digital_ok | ee Shape | 10.777480 | 1.496790 | 10.777480 | 0.746911 | 3.431589 | -1.791458 | 8.664141 | -1.664522 | -0.313343 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 146 | N14 | digital_ok | ee Shape | 11.328301 | 1.551096 | 11.328301 | 0.483450 | 3.320477 | -1.796558 | 7.485413 | -1.396030 | 0.353600 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 146 | N14 | digital_ok | ee Shape | 11.939081 | 11.939081 | 1.563559 | 3.249539 | 0.480914 | 6.330426 | -1.556103 | 0.230730 | -1.686125 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 146 | N14 | digital_ok | ee Shape | 11.919889 | 1.661008 | 11.919889 | 0.540458 | 3.462864 | -1.382163 | 6.835219 | -1.596136 | 0.202401 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 146 | N14 | digital_ok | ee Shape | 11.719955 | 1.674757 | 11.719955 | 1.146386 | 3.546029 | -0.520301 | 7.897213 | -1.873263 | 0.165736 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 146 | N14 | digital_ok | ee Temporal Variability | 12.234494 | 2.062647 | 11.222831 | 1.044269 | 3.487207 | -1.216437 | 12.234494 | -1.286590 | -0.007419 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 146 | N14 | digital_ok | ee Shape | 10.976570 | 2.010965 | 10.976570 | 1.329753 | 3.835416 | -0.601528 | 8.933279 | -2.563745 | 0.713915 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 146 | N14 | digital_ok | ee Shape | 11.700894 | 2.170030 | 11.700894 | 1.011987 | 3.892998 | -0.505260 | 7.174490 | -2.686552 | 0.887067 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 146 | N14 | digital_ok | ee Shape | 13.301611 | 13.301611 | 2.702526 | 4.079172 | 1.429914 | 7.761811 | -0.676198 | 0.361811 | -1.672315 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 146 | N14 | digital_ok | nn Shape | 1.664788 | 0.699534 | 1.664788 | -1.522950 | 0.662176 | 0.845884 | -1.169747 | 1.446195 | -1.899978 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 146 | N14 | digital_ok | nn Shape | 1.459163 | 0.113182 | 1.459163 | -1.445085 | 0.688898 | 0.438335 | -0.920086 | 0.321857 | -1.798308 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 146 | N14 | digital_ok | nn Shape | 1.152016 | 1.152016 | 0.227786 | 0.747333 | -1.633162 | -1.324248 | -0.071346 | -2.247662 | -0.002536 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 146 | N14 | digital_ok | nn Shape | 1.188359 | 1.188359 | 0.275121 | 0.914850 | -1.668448 | -0.955651 | -0.624371 | -2.282454 | 0.334594 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 146 | N14 | digital_ok | nn Shape | 1.329176 | 1.329176 | -0.004576 | 0.937330 | -1.671049 | -1.531837 | -0.794360 | -1.639437 | -0.043724 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 146 | N14 | digital_ok | nn Power | 1.833703 | 0.318098 | 0.958281 | -1.726955 | 1.833703 | -0.421830 | -0.171725 | -1.884193 | 0.484220 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 146 | N14 | digital_ok | nn Shape | 1.217780 | 1.217780 | 0.333531 | 0.856678 | -1.529503 | -1.194829 | -0.448507 | -2.199507 | 0.256120 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 146 | N14 | digital_ok | nn Shape | 1.300633 | -0.112452 | 1.300633 | -1.606534 | 0.678657 | -0.533525 | -1.213404 | 0.008464 | -2.530768 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 146 | N14 | digital_ok | nn Shape | 1.309914 | 1.309914 | 0.011255 | 0.966248 | -1.892218 | -1.003823 | 0.669324 | -2.405611 | -0.105448 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 146 | N14 | digital_ok | nn Power | 1.170873 | -0.067478 | 1.021531 | -1.860822 | 1.170873 | 0.442497 | -1.141120 | -0.004066 | -1.913772 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 146 | N14 | digital_ok | nn Power | 1.730112 | 1.046265 | 0.248244 | 1.730112 | -1.926435 | -1.064364 | 0.077643 | -1.087683 | 0.708997 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 146 | N14 | digital_ok | nn Shape | 1.151233 | 0.208538 | 1.151233 | -1.911166 | 1.104184 | -0.509165 | -1.281256 | -0.057743 | -2.706080 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 146 | N14 | digital_ok | nn Power | 1.536025 | 0.906360 | 0.045160 | 1.536025 | -1.857964 | -1.033774 | -0.596093 | -0.784324 | -0.760842 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 146 | N14 | digital_ok | nn Power | 0.780660 | 0.763230 | 0.006585 | 0.780660 | -1.748722 | -1.650748 | -1.218903 | -1.533122 | -0.094876 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 146 | N14 | digital_ok | nn Power | 1.145866 | -0.638546 | 0.945657 | -1.518109 | 1.145866 | -2.024161 | 0.308826 | -1.451006 | -0.282474 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 146 | N14 | digital_ok | nn Power | 30.100266 | 3.150252 | 2.232008 | 30.100266 | 7.137951 | 5.432572 | 1.774593 | 5.182172 | 2.236833 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 146 | N14 | digital_ok | nn Temporal Variability | 5.239465 | 4.619191 | -0.681848 | 4.661808 | -0.105304 | 5.239465 | -0.446535 | -3.406010 | -0.763065 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 146 | N14 | digital_ok | nn Power | 41.419800 | 7.462584 | 1.057583 | 41.419800 | 17.312333 | 7.541308 | 1.240675 | -6.293574 | -0.650059 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 146 | N14 | digital_ok | nn Power | 48.558674 | 12.043612 | 1.535877 | 48.558674 | 19.204940 | 10.541547 | 2.383712 | -2.773263 | 1.593897 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 146 | N14 | digital_ok | nn Power | 54.818014 | 7.754697 | 0.582591 | 54.818014 | 21.797512 | 20.338903 | 2.956958 | 1.621760 | 0.378033 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 146 | N14 | digital_ok | nn Power | 44.378717 | 8.437421 | 0.721808 | 44.378717 | 17.791784 | 6.506531 | 2.158732 | -3.877408 | -0.321799 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 146 | N14 | digital_ok | nn Shape | 3.426176 | 3.426176 | -1.288895 | 1.868955 | -2.337281 | -0.330627 | -0.296760 | -4.539379 | -0.733047 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 146 | N14 | digital_ok | nn Power | 51.565712 | 7.399156 | 4.384917 | 51.565712 | 41.581275 | 12.850746 | 4.611827 | -6.882099 | -3.632006 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 146 | N14 | digital_ok | nn Power | 35.724585 | 4.649231 | 7.145446 | 29.695605 | 35.724585 | 10.443904 | 35.229634 | -3.177434 | -5.970377 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 146 | N14 | digital_ok | nn Power | 48.771853 | 4.833955 | 7.219080 | 40.104959 | 48.771853 | 4.108412 | 17.715354 | -0.725041 | 4.061925 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 146 | N14 | digital_ok | nn Power | 24.971760 | 6.874515 | 10.232680 | 20.682236 | 24.971760 | 5.383243 | 22.348143 | -2.591554 | -4.859304 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 146 | N14 | digital_ok | nn Power | 33.605619 | 4.988978 | 7.218835 | 27.824572 | 33.605619 | 2.237567 | 9.191835 | -1.990780 | -3.041155 |